A domain-transformed surrogate-assisted differential evolutionary algorithm for hyperparameter optimisation of satellite handover strategy

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhe Yang , Libao Deng , Chunlei Li , Yifan Qin , LiLi Zhang
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引用次数: 0

Abstract

With the rapid advancement of low-Earth-orbit (LEO) satellite technology, satellite phone calls have become increasingly widespread. However, this progress introduces new challenges: the high velocity of LEO satellites necessitates frequent reconnections between terminals and satellites, which can adversely affect communication quality. Moreover, the frequent signal measurements required to maintain connectivity significantly increase the energy consumption of the terminals. To address these challenges, this paper proposes a terminal measurement and satellite-switching strategy. The strategy aims to minimize energy consumption during switching while preserving a high level of user experience. The strategy is based on predictions of satellite cell visibility time and beam visibility time. To further optimise the hyperparameters of this method, we established a simulation-based hyperparameter optimisation model and designed a domain-transformed surrogate-assisted differential evolutionary algorithm (DT-SADE). The algorithm utilises a radial basis function (RBF) model as a surrogate for global search and a Kriging model for local search, and uses domain transformation to address discrepancies between the simulation and surrogate models. Experimental results demonstrate that the proposed method outperforms comparative algorithms across multiple performance metrics, and that hyperparameter optimisation further enhances its performance, highlighting the significance and effectiveness of hyperparameter optimisation.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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